Position geometric error modeling, identification and compensation for large 5-axis machining center prototype

Zhi, Gu; Wang, Chaoqun; Yang, Shoufeng; Zheng, Enlai; Ge, Yaojun

doi:10.1016/j.ijmachtools.2014.10.009

Cited by 76 publications

(31 citation statements)

References 29 publications

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“…A linear least-squares fit [18] was adopted to compare the prediction precisions of the previously proposed regression models and the presently proposed BPANN model. The order of the linear fit was determined by inspecting the residual errors of the approximation via the MSE [27,28].…”

Section: Resultsmentioning

confidence: 99%

Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

2017

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Featured Application: The method employing the artificial neural network proposed in this study for predicting the material removal rate in ultrasonic machining can be considered as a guide for modelling complex general machining problems without explicit mathematical functions. Abstract:The present study proposes a back propagation artificial neural network (BPANN) to provide improved precision for predicting the material removal rate (MRR) in ultrasonic machining. The BPANN benefits from the advantage of artificial neural networks (ANNs) in dealing with complex input-output relationships without explicit mathematical functions. In our previous study, a conventional linear regression model and improved nonlinear regression model were established for modelling the MRR in ultrasonic machining to reflect the influence of machining parameters on process response. In the present work, we quantitatively compare the prediction precision obtained by the previously proposed regression models and the presently proposed BPANN model. The results of detailed analyses indicate that the BPANN model provided the highest prediction precision of the three models considered. The present work makes a positive contribution to expanding the applications of ANNs and can be considered as a guide for modelling complex problems of general machining.

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Section: Resultsmentioning

confidence: 99%

Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

2017

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“…The structures of L5AMC are showed in Figure 1, which is designed to have 40m×6m×4m large translational axes, with ±110° swing angle of the B-axis, ±360° turning angle of the C-axis and is a TTTRR type machine tool [1]. The composite framework is one of the most important components of the L5AMC.…”

Section: Overall Structuresmentioning

confidence: 99%

“…Due to the daily increase in complexity of industrial manufacturing, the demands on large high speed machine tools are increasing rapidly in automotive, aerospace, die making and many other industries along with large free-form surface parts growing demands [1]. Over the last few decades, to improve the accuracy, stiffness and dynamic performance of machine tools, various lightweight and optimization design methods and strategies have been investigated by many researchers at the early design stage, structural optimization and lightweight methods have been successfully applied in optimizing dynamical systems and lightweight machines [2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Composite Framework Design Method for Large 5-axis Machining Center Based on Finite Element Analysis

Zhong¹,

Xu²,

Yang³

2017

dtetr

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Abstract. The subject of this study is to find a best solution to design and manufacture a composite framework for large 5-axis machining center to meet the large dimensions in x, y and z working space, high accuracy for position of machine tool, lightweight design for good dynamic drive performance and cost effective design. A series of parameter studies and finite element analyses were carried out for this purpose. The goal of optimization design is to minimize the displacements in the milling head while a lightweight design, a reliable fabrication processes and a low material cost. A two-step approach was presented to find out the optimum structure of composite framework using finite element analysis method for a low-stiffness-case and a high-stiffness-case to analyze the maximum deformation of milling head under operational loads, including the gravity and maximum acceleration in each axis for starting and stopping. The results show that, after optimization design for composite framework, the deformations in the milling head were below 0.21 mm in high-stiffness-case and the stress inside the laminate was much lower than allowable material limits, it means that the optimal design of composite framework is feasible and successful. The prototype of 40m×6m×4m large 5-axis machining center was also manufactured and the actual verification results indicate that the composite framework can fully satisfy the design requirements of large 5-axis machining center based on the approach proposed by this work, which can provide a technical guidance for design of large structure to improve its precision.

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“…Finally, the interpolation method is used to control the motions of three translational and two rotational joints during machining. The use of curve segments can significantly reduce the nonlinear error directly caused by the motion of translational axes [3]. However, since a nonlinear relationship exists between the position coordinates of the rotation axes and components of tool orientation, the tool will deviate from the plane generated by the initial and final tool orientations during machining and will travel in a curved trajectory instead of the desired straight path, leading to nonlinear error [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Nonlinear Error Caused by Motions of Rotation Axes for Five-Axis Machine Tools with Orthogonal Configuration

Geng

Qiu

2018

Mathematical Problems in Engineering

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Since a nonlinear relationship exists between the position coordinates of the rotation axes and components of the tool orientation, the tool will deviate from the required plane, resulting in nonlinear errors and deterioration of machining accuracy. Few attempts have been made to obtain a general formula and common rules for nonlinear error because of the existence of various kinematic structures of machine tools with orthogonal configuration. This paper analyzes the relationship between the deviation of cutter location points and motion of tool orientations. Five-axis CNC machine tools are divided into two groups according to the configuration of the two rotational joints and the home position. Motions of the tool are regarded as a combination of translation and rotation. A model for error calculation is then built. The maximum deviation of the tool with respect to the reference plane generated by the initial and the final orientation is used to quantify the magnitude of the errors. General formulas are derived and common change rules are analyzed. Finally, machining experiment is conducted to validate the theoretical analysis. The research has important implications on the selection of a particular kinematic configuration that may achieve higher accuracy for a specific machining task.

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Position geometric error modeling, identification and compensation for large 5-axis machining center prototype

Cited by 76 publications

References 29 publications

Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

Precision Obtained Using an Artificial Neural Network for Predicting the Material Removal Rate in Ultrasonic Machining

Composite Framework Design Method for Large 5-axis Machining Center Based on Finite Element Analysis

Analysis of Nonlinear Error Caused by Motions of Rotation Axes for Five-Axis Machine Tools with Orthogonal Configuration

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